Identification of Partially Resolved Objects in Space Imagery with Convolutional Neural Networks

Christopher A. Ertl1, John A. Christian1
1Rensselaer Polytechnic Institute, Troy, USA

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Ali, H., Awan, A.A., Khan, S., Shafique, O., ur Rahman, A., Khan, S.: Supervised classification for object identification in urban areas using satellite imagery. In: 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET). https://doi.org/10.1109/ICOMET.2018.8346383, pp 1–4. IEEE (2018)

Andreon, S., Gargiulo, G., Longo, G., Tagliaferri, R., Capuano, N.: Wide field imaging—I. Applications of neural networks to object detection and star/galaxy classification. Monthly Notices of the Royal Astronomical Society 319(3), 700–716 (2000). https://doi.org/10.1046/j.1365-8711.2000.03700.x

Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (surf). Computer Vision and Image Understanding 110(3), 346–359 (2008). https://doi.org/10.1016/j.cviu.2007.09.014

Bosch, A., Zisserman, A., Munoz, X.: Image classification using random forests and ferns. In: 2007 IEEE 11th International Conference on Computer Vision. https://doi.org/10.1109/ICCV.2007.4409066, pp 1–8. IEEE (2007)

Chang, L., Duarte, M.M., Sucar, L.E., Morales, E.F.: A bayesian approach for object classification based on clusters of sift local features. Expert Systems With Applications 39(2), 1679–1686 (2012). https://doi.org/10.1016/j.eswa.2011.06.059

Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12, 2493–2537 (2011)

Dahl, G.E., Sainath, T.N., Hinton, G.E.: Improving deep neural networks for lvcsr using rectified linear units and dropout. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. https://doi.org/10.1109/ICASSP.2013.6639346, pp 8609–8613 (2013)

Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009. https://doi.org/10.1109/CVPR.2009.5206848, pp 248–255. IEEE (2009)

Déniz, O., Bueno, G., Salido, J., De la Torre, F.: Face recognition using histograms of oriented gradients. Pattern Recogn. Lett. 32(12), 1598–1603 (2011). https://doi.org/10.1016/j.patrec.2011.01.004

Dieleman, S., Willett, K.W., Dambre, J.: Rotation-invariant convolutional neural networks for galaxy morphology prediction. Mon. Not. R. Astron. Soc. 450 (2), 1441–1459 (2015). https://doi.org/10.1093/mnras/stv632

Domingues, R., Michiardi, P., Zouaoui, J., Filippone, M.: Deep gaussian process autoencoders for novelty detection. Mach. Learn. 107(8), 1363–1383 (2018). https://doi.org/10.1007/s10994-018-5723-3

Ertl, C., Christian, J.: Identification of partially resolved objects in space imagery with neural networks. In: AAS/AIAA Astrodynamics Specialist Conference. Snowbird, UT (2018)

Fix, E., Hodges, J.L. Jr: Discriminatory analysis-nonparametric discrimination: consistency properties. Tech. rep., California Univ Berkeley (1951)

Franco-Lopez, H., Ek, A.R., Bauer, M.E.: Estimation and mapping of forest stand density, volume, and cover type using the k-nearest neighbors method. Remote Sens. Environ. 77(3), 251–274 (2001). https://doi.org/10.1016/S0034-4257(01)00209-7

Friedman, A.M., Fan, S., Frueh, C.: Light curve inversion observability analysis. In: 2019 AAS Astrodynamics Specialist Conference (2019)

Fukushima, K.: Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics 36(4), 193–202 (1980). https://doi.org/10.1007/BF00344251

Furfaro, R., Linares, R., Reddy, V.: Space objects classification via light-curve measurements: deep convolutional neural networks and model-based transfer learning. In: AMOS Technologies Conference, Maui Economic Development Board, Kihei, Maui, HI (2018)

Gaskell, R., Saito, J., Ishiguro, M., Kubota, T., Hashimoto, T., Hirata, N., Abe, S., Barnouin-Jha, O., Scheeres, D.: Gaskell itokawa shape model v1.0. NASA Planetary Data System 92, HAY-A-AMICA-5-ITOKAWASHAPE-V1.0 (2008)

Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016). http://www.deeplearningbook.org

Han, S., Pool, J., Tran, J., Dally, W.: Learning both weights and connections for efficient neural network. In: Advances in Neural Information Processing Systems 28. Montreal, Canada (2015)

He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/CVPR.2016.90 (2016)

Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors. arXiv:1207.0580 (2012)

Ho, T.K.: Random decision forests. In: Proceedings of 3rd International Conference on Document Analysis and Recognition. https://doi.org/10.1109/ICDAR.1995.598994, vol. 1, pp 278–282. IEEE (1995)

Hoffer, E., Hubara, I., Soudry, D.: Train longer, generalize better: closing the generalization gap in large batch training of neural networks. In: Advances in Neural Information Processing Systems 30. Longbeach, CA (2017)

Huang, G.B., Saratchandran, P., Sundararajan, N.: A generalized growing and pruning rbf (ggap-rbf) neural network for function approximation. IEEE Trans. Neural Netw. 16(1), 57–67 (2005). https://doi.org/10.1109/TNN.2004.836241

Jacobs, C., Glazebrook, K., Collett, T., More, A., McCarthy, C.: Finding strong lenses in CFHTLS using convolutional neural networks. Mon. Not. R. Astron. Soc. 471(1), 167–181 (2017). https://doi.org/10.1093/mnras/stx1492

Jia, B., Pham, K.D., Blasch, E., Wang, Z., Shen, D., Chen, G.: Space object classification using deep neural networks. In: 2018 IEEE Aerospace Conference. https://doi.org/10.1109/AERO.2018.8396567, pp 1–8. IEEE (2018)

Jones, R.C.: On the point and line spread functions of photographic images. J. Opt. Soc. Am. 48(12), 934–937 (1958). https://doi.org/10.1364/JOSA.48.000934

Kaasalainen, M., Torppa, J.: Optimization methods for asteroid lightcurve inversion: i. shape determination. Icarus 153(1), 24–36 (2001). https://doi.org/10.1006/icar.2001.6673

Kaasalainen, M., Torppa, J., Muinonen, K.: Optimization methods for asteroid lightcurve inversion: Ii. the complete inverse problem. Icarus 153(1), 37–51 (2001). https://doi.org/10.1006/icar.2001.6674

Kheradpisheh, S.R., Ganjtabesh, M., Thorpe, S.J., Masquelier, T.: Stdp-based spiking deep convolutional neural networks for object recognition. Neural Networks 99, 56–67 (2018). https://doi.org/10.1016/j.neunet.2017.12.005

Kim, D., Dahyot, R.: Face components detection using surf descriptors and svms. In: 2008 International Machine Vision and Image Processing Conference. https://doi.org/10.1109/IMVIP.2008.15, pp 51–56. IEEE (2008)

Kim, E.J., Brunner, R.J.: Star-galaxy classification using deep convolutional neural networks. Mon. Not. R. Astron. Soc., 4463–4475. https://doi.org/10.1093/mnras/stw2672 (2016)

Korytkowski, M., Rutkowski, L., Scherer, R.: Fast image classification by boosting fuzzy classifiers. Inform. Sci. 327, 175–182 (2016). https://doi.org/10.1016/j.ins.2015.08.030

Krishnamurthy, V., Levoy, M.: Fitting smooth surfaces to dense polygon meshes. In: Proceedings of the 23rd Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH ’96. https://doi.org/10.1145/237170.237270, pp 313–324. ACM, New York (1996)

Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 25, pp 1097–1105. Curran Associates, Inc. (2012)

LeCun, Y., Boser, B.E., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W.E., Jackel, L.D.: Handwritten digit recognition with a back-propagation network. In: Advances in Neural Information Processing Systems, pp 396–404 (1990)

LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998). https://doi.org/10.1109/5.726791

LeCun, Y., Cortes, C., Burges, C.: Mnist handwritten digit database. AT&T Labs [Online]. Available: http://yann.lecun.com/exdb/mnist2 (2010)

Lin, T., Goyal, P., Girshick, R., He, K., Dollar, P.: Focal loss for dense object detection. IEEE Trans. Pattern Anal. Mach. Intell., 1–1. https://doi.org/10.1109/TPAMI.2018.2858826 (2018)

Linares, R., Furfaro, R.: Space object classification using deep convolutional neural networks. In: 2016 19th International Conference on Information Fusion (FUSION). https://doi.org/10.1109/AERO.2018.8396567, pp 1140–1146. IEEE (2016)

Linares, R., Jah, M.K., Crassidis, J.L., Nebelecky, C.K.: Space object shape characterization and tracking using light curve and angles data. Journal of Guidance, Control, and Dynamics 37(1), 13–25 (2013). https://doi.org/10.2514/1.62986

Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004). https://doi.org/10.1023/B:VISI.0000029664.99615.94

Lu, H., Li, Y., Chen, M., Kim, H., Serikawa, S.: Brain intelligence: go beyond artificial intelligence. Mobile Networks and Applications, 1–8. https://doi.org/10.1007/s11036-017-0932-8 (2017)

Lu, X.P., Huang, X.J., Ip, W.H., Hsia, C.H.: Lebedev acceleration and comparison of different photometric models in the inversion of lightcurves for asteroids. Planet. Space Sci. 153, 1–10 (2018)

McMahon, J.W., Scheeres, D.J.: Shape estimation from lightcurves including constraints from orbit determination. In: Advanced Maui Optical and Space Surveillance Technologies Conference (2016)

Mou, L., Zhu, X.X.: RiFCN: recurrent network in fully convolutional network for semantic segmentation of high resolution remote sensing images. CoRR arXiv:1805.02091 (2018)

Pal, M.: Random forest classifier for remote sensing classification. Int. J. Remote Sens. 26(1), 217–222 (2005). https://doi.org/10.1080/01431160412331269698

Piccinini, P., Prati, A., Cucchiara, R.: Real-time object detection and localization with sift-based clustering. Image Vis. Comput. 30(8), 573–587 (2012). https://doi.org/10.1016/j.imavis.2012.06.004

Pillai, S., Leonard, J.: Monocular slam supported object recognition. arXiv:1506.01732 (2015)

Pourrahmani, M., Nayyeri, H., Cooray, A.: Lensflow: a convolutional neural network in search of strong gravitational lenses. Astrophys. J. 856(1), 68 (2018). https://doi.org/10.3847/1538-4357/aaae6a

Rao, U.G., Jain, V.: Gaussian and exponential approximations of the modulation transfer function. JOSA 57(9), 1159–1160 (1967)

Rhodes, A., Christian, J.A.: Constructing a 3D scale space from implicit surfaces for vision-based spacecraft relative navigation. In: 41St Annual AAS Guidance, Navigation, and Control Conference. Breckenridge, CO (2018)

Rodriguez-Galiano, V.F., Ghimire, B., Rogan, J., Chica-Olmo, M., Rigol-Sanchez, J.P.: An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS J. Photogramm. Remote. Sens. 67, 93–104 (2012). https://doi.org/10.1016/j.isprsjprs.2011.11.002

Rosten, E., Drummond, T.: Fusing points and lines for high performance tracking. In: Tenth IEEE International Conference on Computer Vision (ICCV’05), vol. 2, pp. 1508–1515. Citeseer. https://doi.org/10.1109/ICCV.2005.104 (2005)

Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323(6088), 533–536 (1986). https://doi.org/10.1038/323533a0

Russell, H.: On the light variations of asteroids and satellites. Astrophys. J. 24, 1–18 (1906). https://doi.org/10.1086/141361

Sabokrou, M., Khalooei, M., Fathy, M., Adeli, E.: Adversarially learned one-class classifier for novelty detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3379–3388. https://doi.org/10.1109/CVPR.2018.00356 (2018)

Schmitt, D., McCoy, N.: Object classification and localization using surf descriptors. CS 229, 1–5 (2011)

Schneider, S., Taylor, G.W., Kremer, S.C.: Deep learning object detection methods for ecological camera trap data. arXiv:1803.10842 (2018)

Schroff, F., Criminisi, A., Zisserman, A.: Object class segmentation using random forests. In: BMVC, pp. 1–10 (2008)

Sutton, R.S., Barto, A.G., Williams, R.J.: Reinforcement learning is direct adaptive optimal control. IEEE Control. Syst. Mag. 12(2), 19–22 (1992). https://doi.org/10.1109/37.126844

Sze, V., Chen, Y.H., Yang, T.J., Emer, J.S.: Efficient processing of deep neural networks: a tutorial and survey. Proc. IEEE 105(12), 2295–2329 (2017). https://doi.org/10.1109/JPROC.2017.2761740

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/CVPR.2015.7298594 (2015)

Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/CVPR.2016.308(2016)

Thanh Noi, P., Kappas, M.: Comparison of random forest, k-nearest neighbor, and support vector machine classifiers for land cover classification using sentinel-2 imagery. Sensors 18(1), 18 (2018). https://doi.org/10.3390/s18010018

Turk, G., Levoy, M.: Zippered polygon meshes from range images. In: Proceedings of the 21st Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH ’94. https://doi.org/10.1145/192161.192241, pp 311–318. ACM, New York (1994)

Vasilev, A., Golkov, V., Meissner, M., Lipp, I., Sgarlata, E., Tomassini, V., Jones, D.K., Cremers, D.: q-space novelty detection with variational autoencoders. arXiv:1806.02997 (2018)

Ďurech, J., Hanuš, J., Brož, M., Lehký, M., Behrend, R., Antonini, P., Charbonnel, S., Crippa, R., Dubreuil, P., Farroni, G., Kober, G., Lopez, A., Manzini, F., Oey, J., Poncy, R., Rinner, C., Roy, R.: Shape models of asteroids based on lightcurve observations with blueeye600 robotic observatory. Icarus 304, 101–109 (2018). https://doi.org/10.1016/j.icarus.2017.07.005

Wang, N., Yeung, D.Y.: Learning a deep compact image representation for visual tracking. In: Burges, C.J.C., Bottou, L., Welling, M., Ghahramani, Z., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 26, pp 809–817. Curran Associates, Inc. (2013)

Wang, R., Xia, Y., Wang, G., Tian, J.: License plate localization in complex scenes based on oriented fast and rotated brief feature. Journal of Electronic Imaging 24(5), 053011 (2015). https://doi.org/10.1117/1.JEI.24.5.053011

Wang, X., Han, T.X., Yan, S.: An HOG-LBP human detector with partial occlusion handling. In: 2009 IEEE 12th International Conference on Computer Vision. https://doi.org/10.1109/ICCV.2009.5459207, pp 32–39. IEEE (2009)

Widrow, B., Hoff, M.E.: Adaptive switching circuits. Tech. rep., Stanford Univ Ca Stanford Electronics Labs (1960)

Yu, S., De Backer, S., Scheunders, P.: Genetic feature selection combined with composite fuzzy nearest neighbor classifiers for hyperspectral satellite imagery. Pattern Recogn. Lett. 23(1-3), 183–190 (2002). https://doi.org/10.1016/S0167-8655(01)00118-0

Zhang, G.P.: Neural networks for classification: a survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 30(4), 451–462 (2000). https://doi.org/10.1109/5326.897072